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Learning Bidding Strategies in Local Electricity Markets using a Nature-Inspired Algorithm

2020Conference object Open access
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dc.contributor.authorLezama, Fernando
dc.contributor.authorSoares, João
dc.contributor.authorFaia, Ricardo
dc.contributor.authorFaria, Pedro
dc.contributor.authorVale, Zita
dc.date.accessioned2021-09-22T10:57:52Z
dc.date.available2021-09-22T10:57:52Z
dc.date.issued2020
dc.description.abstractLocal electricity markets (LEM) are a promising idea to foster the efficiency and use of renewable energy at the distribution level. However, how these local markets will be integrated into existing market structures, and to make the most profit from them, is still unclear. In this work, we propose a LEM framework based on bi-level optimization. In the upper level, end-users aim at maximizing profits, while the lower level represents the clearing market process. Moreover, a cascade integration to the wholesale market through an aggregator that acts after the LEM has been cleared is considered. Learning strategies using only available information can be a powerful tool to take the most advantage of LEM. To this end, we advocate the use of ant colony optimization (ACO), a nature-inspired technique, similar to that employed in machine learning. By using ACO, consumers, producers and prosumers, can learn the best strategies to maximize their profits without sharing private information and based solely on their experience.pt_PT
dc.description.sponsorshiphis work has received funding from the European Union's Horizon 2020 research and innovation programme under project DOMINOES (grant agreement No 771066), from FEDER Funds through COMPETE program and from National Funds through (FCT) under the projects UIDB/00760/2020, MASSociety (PTDC/EEI-EEE/28954/2017), and grants CEECIND/02887/2017, CEECIND/02814/2017, SFRH/BD/133086/2017.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1109/EEM49802.2020.9221939pt_PT
dc.identifier.isbn978-1-7281-6919-4
dc.identifier.urihttp://hdl.handle.net/10400.22/18470
dc.language.isoengpt_PT
dc.publisherIEEEpt_PT
dc.relationCEECIND/02887/2017pt_PT
dc.relationSmart Distribution Grid: a Market Driven Approach for the Next Generation of Advanced Operation Models and Services
dc.relationMulti-Agent Systems SemantiC Interoperability for simulation and dEcision supporT in complex energY systems
dc.relationApoio à decisão para participação em mercados de energia elétrica
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/9221939pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectAnt Colonypt_PT
dc.subjectOptimizationpt_PT
dc.subjectLearning Strategypt_PT
dc.subjectLocal energy marketpt_PT
dc.subjectRenewable energypt_PT
dc.titleLearning Bidding Strategies in Local Electricity Markets using a Nature-Inspired Algorithmpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardTitleSmart Distribution Grid: a Market Driven Approach for the Next Generation of Advanced Operation Models and Services
oaire.awardTitleMulti-Agent Systems SemantiC Interoperability for simulation and dEcision supporT in complex energY systems
oaire.awardTitleApoio à decisão para participação em mercados de energia elétrica
oaire.awardURIinfo:eu-repo/grantAgreement/EC/H2020/771066/EU
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/9471 - RIDTI/PTDC%2FEEI-EEE%2F28954%2F2017/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT//SFRH%2FBD%2F133086%2F2017/PT
oaire.citation.conferencePlaceStockholm, Swedenpt_PT
oaire.citation.endPage6pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.title17th International Conference on The European Energy Market (EEM20)pt_PT
oaire.fundingStreamH2020
oaire.fundingStream9471 - RIDTI
person.familyNameLezama
person.familyNameSoares
person.familyNameFaia
person.familyNameFaria
person.familyNameVale
person.givenNameFernando
person.givenNameJoão
person.givenNameRicardo Francisco Marcos
person.givenNamePedro
person.givenNameZita
person.identifier1043580
person.identifier78FtZwIAAAAJ
person.identifier632184
person.identifier.ciencia-idE31F-56D6-1E0F
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person.identifier.ciencia-idB212-2309-F9C3
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.orcid0000-0001-8638-8373
person.identifier.orcid0000-0002-4172-4502
person.identifier.orcid0000-0002-1053-7720
person.identifier.orcid0000-0002-5982-8342
person.identifier.orcid0000-0002-4560-9544
person.identifier.ridA-6945-2017
person.identifier.ridA-5824-2012
person.identifier.scopus-author-id36810077500
person.identifier.scopus-author-id35436109600
person.identifier.scopus-author-id7004115775
project.funder.identifierhttp://doi.org/10.13039/501100008530
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameEuropean Commission
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
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